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Title: An In-Depth Analysis of the Chung-Lu Model

In the classic Erd}os R enyi random graph model [5] each edge is chosen with uniform probability and the degree distribution is binomial, limiting the number of graphs that can be modeled using the Erd}os R enyi framework [10]. The Chung-Lu model [1, 2, 3] is an extension of the Erd}os R enyi model that allows for more general degree distributions. The probability of each edge is no longer uniform and is a function of a user-supplied degree sequence, which by design is the expected degree sequence of the model. This property makes it an easy model to work with theoretically and since the Chung-Lu model is a special case of a random graph model with a given degree sequence, many of its properties are well known and have been studied extensively [2, 3, 13, 8, 9]. It is also an attractive null model for many real-world networks, particularly those with power-law degree distributions and it is sometimes used as a benchmark for comparison with other graph generators despite some of its limitations [12, 11]. We know for example, that the average clustering coe cient is too low relative to most real world networks. As well, measures of a nitymore » are also too low relative to most real-world networks of interest. However, despite these limitations or perhaps because of them, the Chung-Lu model provides a basis for comparing new graph models.« less
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  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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Technical Report
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Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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United States